AI Infrastructure Crisis

ChatGPT Loses $38 Million
Every Single Day

OpenAI is projected to lose $14 billion in 2026. The structural problem: every query costs money, and 40–60% of those queries have already been answered. They're running $0.03 inference on questions the model has seen before. Cachee serves the cached answer in 28.9 nanoseconds.

$14B
Projected 2026 loss (OpenAI)
$38M/day
Daily burn rate
40–60%
Queries are semantic duplicates
$547M
Annual savings with 50% cache hit
The Problem

Every Query Runs Full Inference.
Even the Duplicates.

When someone asks "What is the capital of France?" for the 10 millionth time, the model still runs a full forward pass through billions of parameters. GPU time consumed. Electricity burned. Money lost. The answer hasn't changed since the first query.

300ms
Full inference per query
$0.01–$0.06 per response
GPU at 100% utilization
→
28.9ns
Cached response from Cachee
$0.00 per response
GPU not touched
The math that keeps AI CEOs awake

At 100 million queries/day with an average cost of $0.03/query, that's $3 million per day on inference alone. If 50% of those queries are semantic near-duplicates, that's $1.5 million per day spent answering the same questions. Every day. $547 million per year.

The Solution

Semantic Caching at 28.9 Nanoseconds

Cachee sits between your users and your model. It hashes the semantic embedding of every prompt, checks if a similar prompt was answered before, and serves the cached response — skipping inference entirely.

🧠
Semantic Response Cache
Every prompt is embedded and hashed. Cachee checks for semantic similarity against previously answered queries at 28.9ns. Hit? Serve the cached response. Miss? Run inference and cache the result. 40–60% of production queries are near-duplicates.
50% hit rate = $547M/year saved at OpenAI scale
⚡
KV Cache Acceleration
Transformer attention uses Key-Value caches that grow with context length. These live in GPU HBM at $30K per 80GB. Cachee acts as an L1 tier — hot KV pairs served at 28.9ns instead of competing for GPU memory. Reduces the memory pressure driving your GPU bill.
Free up GPU HBM for compute, not storage
🔍
RAG Pipeline Acceleration
Every RAG query hits a vector database (Pinecone, Weaviate, Qdrant) at 5–50ms. Cachee caches hot embeddings and retrieval results at 28.9ns. Faster context retrieval = faster time-to-first-token. Users feel the difference immediately.
1,000x faster context retrieval
💰
Embedding Cache
Computing embeddings for every query costs $0.0001/call on text-embedding-3-large. At 100M queries/day, that's $10K/day just for embeddings. Cache the embeddings. Same text = same embedding. Cachee serves it at 28.9ns.
$3.6M/year saved on embeddings alone
🔄
Multi-Turn Context Cache
Multi-turn conversations repeat context with every message. The first 90% of the prompt is identical to the previous turn. Cachee caches the tokenized context, reducing prompt processing from milliseconds to nanoseconds for every follow-up message.
90% of prompt tokens served from cache
🛡
Post-Quantum Security
Cached AI responses are signed with ML-DSA-65 (Dilithium) attestation — NIST FIPS 204 post-quantum cryptography. Detect tampering, verify response authenticity, and prove cache integrity. No other cache has this. First in the industry.
Quantum-resistant cache attestation
The Math

Dollar-for-Dollar Savings at Every Scale

Conservative model: 50% semantic cache hit rate, $0.03 average inference cost per query, Cachee at $0.0001/1000 cache lookups.

Scale Queries/Day Daily Inference Cost With 50% Cache Hit Daily Savings Annual Savings
Startup 100K $3,000 $1,500 $1,500 $547K
Growth 1M $30,000 $15,000 $15,000 $5.4M
Enterprise 10M $300,000 $150,000 $150,000 $54.7M
OpenAI Scale 100M+ $3,000,000 $1,500,000 $1,500,000 $547M
Why 50% is conservative

Research shows 40–60% of production LLM prompts are semantic near-duplicates. Customer support chatbots see 70%+ repetition. Internal knowledge bases see 80%+. The more specialized the use case, the higher the cache hit rate. Some enterprise deployments achieve 90% hit rates because employees ask the same questions about the same internal documents.

Architecture

Where Cachee Sits in the AI Stack

Cachee deploys as a sidecar or embedded library. Zero changes to your model serving infrastructure. The cache check happens before inference — if it hits, the GPU never fires.

User Prompt
0ms
→
Embed + Hash
~1ms
→
Cachee L0 Check
28.9ns
→
Cache Hit?
50%

✗ Cache Miss → Run Inference

GPU inference300ms
Cost per query$0.03
GPU utilization100%
Then: cache the response for next time

✓ Cache Hit → Serve Instantly

Cachee L0 read28.9ns
Cost per query$0.00
GPU utilization0%
User sees response in <2ms
Who Needs This

Every Company Running LLM Inference

LLM API Providers
OpenAI, Anthropic, Google, Cohere — anyone charging per-token. Every cached response is pure margin instead of GPU cost. At scale, this is the difference between profitability and a $14B loss.
Enterprise AI Deployments
Internal chatbots, knowledge bases, code assistants. Employees ask the same questions about the same documents. 80%+ cache hit rates are common. Your $500K/year OpenAI bill drops to $100K.
RAG Applications
Every retrieval-augmented generation pipeline hits a vector DB for context. Cachee caches hot retrievals at 28.9ns instead of 5–50ms. Time-to-first-token drops dramatically. Users feel the speed.
Customer Support AI
"How do I reset my password?" gets asked 10,000 times a day. The answer is the same every time. Without caching, that's 10,000 inference runs. With Cachee, it's 1 inference + 9,999 cache hits at 28.9ns each.
AI-Powered Search
Perplexity, You.com, AI search engines. Same queries repeat across users. "Best restaurants in NYC" doesn't need fresh inference every 30 seconds. Cache the synthesized answer, refresh on a schedule.
Autonomous Agents
AI agents make thousands of LLM calls per task. Tool selection, planning, and reasoning queries are highly repetitive. Cachee short-circuits the repeated calls, reducing agent costs by 60–80%.
The DeepSeek Effect

Cheaper Models Make Caching
Even More Important

DeepSeek proved frontier AI can be built for a fraction of the cost. But cheaper models don't eliminate the per-query cost — they just lower it. At $0.001/query instead of $0.03/query, you still pay on every single query. Caching eliminates the per-query cost entirely for duplicates.

Model Cost/Query 100M queries/day With 50% Cache Saved/Year
GPT-4o $0.03 $3M/day $1.5M/day $547M
Claude Sonnet $0.015 $1.5M/day $750K/day $273M
DeepSeek V3 $0.001 $100K/day $50K/day $18.2M
Self-hosted (GPU amortized) $0.005 $500K/day $250K/day $91.2M
The insight

Even at DeepSeek's radically lower inference costs, 100M queries/day still costs $100K/day without caching. Cachee cuts that to $50K/day. The cheaper the model, the more the margin goes to whoever can eliminate redundant compute. That's us.

$38 million a day on inference.
$0 per cached response.

The AI industry's biggest cost problem has a 28.9-nanosecond solution.
The companies that figure this out first win.

See The Full Math → Live Speed Test → Start Free Trial

Your model is brilliant.
Your cache should be too.

Deploy Cachee in 15 minutes. No model changes. No infrastructure migration. Just fewer GPU cycles wasted on answers you already have.

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